关键词: 特征选择, 线性判别分析, 分类

Abstract: The paper proposed a new approach of feature selection based on Constrained Linear Discriminant Analysis (CLDA), which modeled feature selection as a search problem in subspace and made optimal solution subject to some restrictions. Furthermore, CLDA optimization problem was transformed into a process of scoring and sorting features. Experiments on UCI machine learning repository and Reuters-21578 dataset show that the proposed approach can consistently obtain better results with fewer features than that with all features.

Key words: feature selection, Linear Discriminant Analysis (LDA), categorization